Probabilistic Models and Inference

The course will focus on advanced probabilistic inference and modelling techniques, complementing the core 'Probabilistic AI & reasoning' course, and causality. On the inference side, the topics include: representation of uncertainty (Bayesian networks, undirected graphical models, probabilistic programs); exact inference algorithms for Bayesian networks; approximate inference algorithms (importance sampling, metropolis-hastings, particle filtering, variational inference); implementation strategies for probabilistic programs; learning for probabilistic inference; discrete probabilistic programming; types of probabilistic queries; algorithms for causal inference. On the modelling side, the course will include a spectrum of common probabilistic models that are often used in practice.